Most genetic studies look for genes that influence individual traits, such as body weight or blood pressure. However, there are many situations in which traits are correlated with each other and, further, these patterns often run in families. These correlations may reflect underlying shared genetic effects. Studies that focus on identifying genes that influence clusters of traits provide an important and innovative approach to studying the genetic basis of common, complex diseases. The Metabolic Syndrome (MetS) is characterized by a cluster of traits, including overall and central obesity, lipid abnormalities, hypertension, glucose intolerance, and insulin resistance. Further, MetS is an important and growing public health problem, currently affecting 34% of the U.S. population and associated with increased risk for cardiovascular disease, stroke, and type 2 diabetes. Although it is clear that the individual MetS traits are genetically influenced, less is known about whether genes affect the clustering of traits that characterize MetS. For example, is the clustering due to: 1) individual gene(s) that affect more than one MetS trait at a time (pleiotropy), or 2) several closely linked genes that each affect the different individual traits (co-incident linkage), or 3) combination of both mechanisms? MetS is particularly well suited for evaluating these effects and understanding the underlying genetic architecture of this complex disease. Using family data from a multi-ethnic study, we previously identified three chromosomal regions with strong evidence for linkage to specific combinations of MetS traits, and also found evidence of both pleiotropy and coincident linkage. The goal of this project is to identify the specific variants tat underlie the linkage signals for clusters of MetS traits. This will be accomplished through four specific aims and focusing on the subset of families with prior evidence for linkage. We will work with the Nickerson laboratory and Dr. Bruce Weir to incorporate second generation sequencing of protein coding regions (exomes) and targeted genotyping into our existing study. We will also evaluate heterogeneity using the four different racial/ethnic groups collected as part of our existing study. We expect to identify the specific genes and variants that are responsible for the linkage signals. Identifying genes that influence several established cardiovascular disease risk factors would provide an important new drug target that could have significant implications for translation to clinical practice. Public Health Impact and Significance: Identifying genes and specific variants that increase risk of multiple MetS features in several ethnic/racial groups broadens the potential public health impact of this project. Potential Clinical Impact and Significance: Identifying genes that influence several established risk factors simultaneously would provide important new drug targets.